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The paper introduces Meta‑Diff, a method that learns a reusable source‑to‑target relation in a compact task space, enabling rapid adaptation of a diffusion model to new targets with minimal or no target data. By encoding each distribution into a low‑dimensional coordinate and transporting a source coordinate to its predicted target, the approach avoids costly fine‑tuning and achieves significant performance gains on CIFAR‑100 and Office‑Home benchmarks. Experiments show that when task identity is preserved, the relation generalizes across tasks, and the source‑target shift is manageable, Meta‑Diff can outperform target‑only modeling and full fine‑tuning. whyItMatters:"The method offers a practical solution for few‑shot adaptation in data‑scarce settings, such as healthcare, by reducing computational cost and improving reliability compared to traditional retraining approaches."
arXiv:2605. 31191v2 Announce Type: replace Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on CIFAR-10.
Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied across scenes. Manually selecting the best configuration, however, can be time-consuming and not always practical.
arXiv:2610.34092v1 Announce Type: new Abstract: Few-step distillation for autoregressive (AR) video generation commonly first initializes an AR student and then refines it with distribution matching...
PairCLIP‑SWA is a single‑backbone deepfake detector that fine‑tunes an OpenCLIP ViT‑L/14 visual tower using a class‑balanced corpus created by CLIP‑similarity‑guided subsampling and then applies stochastic weight averaging over the fine‑tuning trajectory. Trained on the IEEE SP Cup 2025 (DF‑Wild‑Cup) benchmark, the weight‑averaged model achieves 97.04 % accuracy, 98.64 % AU‑ROC and a 4.07 % equal error rate—outperforming the best published three‑backbone ensemble. The study shows that averaging stabilizes validation performance, prevents misranking on disjoint test sets, and that CLIP pre‑training, rather than ensembling, is the key driver of accuracy.
arXiv:2610.11146v3 Announce Type: replace Abstract: Training-only teacher access lets a compact student deploy with local features and exported weights. This established distillation interface does n...